Machine Learning API Engineer
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- Build and test a reliable ML inference API
- Version models and data, and ship through CI/CD
- Scale, secure and operate production inference safely
Benchmark: Cross-industry ML inference and model-serving signals (current Oct 2026) Mastery ladder: 2 current-market competencies per level; 2 substantive learning bridges per competency (12 total).
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Build and test a reliable ML inference API
API and model-serving fundamentals
Coverage: Python / APIs & Backend Engineering / Model Serving
ML fundamentals, input validation and testing
Coverage: Machine Learning / Software Testing & Quality Engineering
Version models and data, and ship through CI/CD
Model/data versioning and experiment tracking
Coverage: MLOps / Experiment Tracking / Reproducibility
Containers and CI/CD for ML services
Coverage: Docker / CI/CD / MLOps
Scale, secure and operate production inference safely
Scaling, latency and reliable serving
Coverage: MLOps & Model Serving / Systems Performance
Production MLOps pipeline, AWS and rollback
Coverage: MLOps / AWS / Production Deployment
Role-Readiness Proof / Capstone
Deploy a production ML prediction API: build a versioned inference endpoint with schema validation, authentication and failure-case handling; containerize it and deploy through CI/CD; instrument latency, errors and rollback; then compare two model versions with a documented quality, reliability and cost review.
Proof Rubric
Demonstrate reliable API design, reproducible model serving, disciplined testing and containerized CI/CD delivery. Show scaling, monitoring, security and rollback judgment, and quantify latency, throughput, reliability and cost tradeoffs between model versions.